Improve visualizations

Communicating diet composition and trends

Diet information is complicated, with lots of components. Showing a complex thing changing over time can be even more complicated. Here are some trial plots using the datasets we already have attempting to simplify the message for the audience, without loss of information.

Diet composition

We can start with our more simple diet compositions which use only four prey categories: benthic invertebrates (BENINV), fish (FISH), pelagic invertebrates (PELINV), and other prey (OTHER). Here we read in the two datasets with these simpler prey categories, using more descriptive names than before to make our code clearer in these comparisons:

library(here)
library(tidyverse)
library(ecodata)
library(patchwork)

preddiet4prey <- read.csv(here('data/allwt2.csv')) #diet for individual predators

aggdiet4prey <- read.csv(here('data/agg.pred.csv')) #aggregated diet across all predators

We will define the colors used for the prey categories and use them consistently throughout the analyses:

preycol <- c(  #make object preycol by combining this list of hex codes
  "#3b6100",
  "#6481fc",
  "#6ce87e",
  "#c152c1")

We will need to have a full list of which prey are included in each category, because this question will inevitably come up.

Our initial plots for diet composition show the percent of each category in the diet each year as a stacked bar plot.

Here is the function that makes that plot. I’ve generalized it so it can be used for both the individual and aggregated datasets. That means the species will need to be defined by filtering the input data, and the epu and or season can also optionally be filtered at that step. I’ve also reordered seasons more intuitively to have spring first and fall second, and epus to be arranged from north to south with Scotian Shelf SS first, then Gulf of Maine GOM, Georges Bank GB, and Mid-Atlantic Bight MAB:

plotDietCompBar <- function(dat, metric, title=NULL){   #defines the name of the function and the requied inputs
  
dat %>% 
  filter(!is.na(.data[[metric]])) %>%          #take out the NA values (literally "not is NA")
  ggplot(aes(x=year, y=.data[[metric]], fill=prey)) +     #year on x axis, %diet comp on y
  geom_bar(width = 1, stat = "identity") +    #stacked bar chart
  scale_fill_manual(values=preycol) +         #custom colors
  ecodata::theme_facet() +                    #simplify
  facet_grid(fct_relevel(epu, "SS", "GOM", "GB", "MAB")~
               fct_relevel(season, "SPRING", "FALL")) + #separate by ordered epu and season
  labs(y = "% diet composition",              #add sensible labels
       title = title) 
    
}   

Here is a test with aggregated predators:

plotDietCompBar(dat=aggdiet4prey, metric="rave_amt", title="Test All")

Here is a figure with only one season and two EPUs for aggregated predators:

plotDietCompBar(dat=aggdiet4prey%>%filter(season=="SPRING", epu %in% c("GOM", "GB")), 
                metric="rave_amt", 
                title="All predators combined, Spring, New England")

Another example with a single season and EPU for aggregated predators:

plotDietCompBar(dat=aggdiet4prey%>%filter(season=="FALL", epu %in% c("MAB")), 
                metric="rave_amt", 
                title="All predators combined, Fall, Mid-Atlantic")

Here is a test with an individual predator using the same function. Note that the filter statement includes the predator name:

plotDietCompBar(dat=preddiet4prey%>%filter(comname=="SUMMER FLOUNDER"), 
                metric="relmsw", 
                title="Summer Flounder")

Trends

Here is a new trends plot function that re-orients the prey percent plots into a column (similar to a stacked bar plot), uses the same set of colors for the prey, and can also be used on either dataset.

plotPreytrend <- function(dat, metric, title=NULL){   #defines the name of the function and the requied inputs
  
dat %>% 
  filter(!is.na(.data[[metric]])) %>%          #take out the NA values (literally "not is NA")
  ggplot(aes(x=year, y=.data[[metric]], colour=prey)) +     #year on x axis, %diet comp on y
  geom_point(alpha=0.5) +                              #proportion in each year is a point
  scale_colour_manual(values=preycol) +          #custom colors
  ecodata::geom_gls(aes(x=year, y=.data[[metric]]), warn = FALSE) + #prints lines if significant trend
  ecodata::theme_facet() +                   #a simpler theme, easier to read
  facet_grid(fct_relevel(epu, "SS", "GOM", "GB", "MAB")~prey~
             fct_relevel(season, "SPRING", "FALL"),                 #separate by epu, season, and prey
             labeller = labeller(.multi_line = FALSE)) +      #format labels              
  theme(strip.text.x = element_text(size = 8)) +  #plot titles smaller
  labs(y = "% diet composition",
       title = title)               #add sensible labels

}   

Test the trend plot for all predators combined. This is a bit much, but it does what we tell it:

plotPreytrend(dat=aggdiet4prey, metric="rave_amt", title="Test All")

A smaller subset is easier to look at:

plotPreytrend(dat=aggdiet4prey%>%filter(season=="SPRING", epu %in% c("GOM", "GB")), 
                metric="rave_amt", 
                title="All predators combined, Spring, New England")

To plot the areas side by side we need two function calls:

p1 <- plotPreytrend(dat=aggdiet4prey%>%filter(season=="SPRING", epu %in% c("GOM")), 
                metric="rave_amt", 
                title="All predators, Gulf of Maine")

p2 <- plotPreytrend(dat=aggdiet4prey%>%filter(season=="SPRING", epu %in% c("GB")), 
                metric="rave_amt", 
                title="All predators, Georges Bank")

p1 + p2 + plot_layout(guides = 'collect', ncol = 2)

But probably best if it is one area, possibly two seasons:

plotPreytrend(dat=aggdiet4prey%>%filter(epu %in% c("MAB")), 
                metric="rave_amt", 
                title="All predators combined, Mid-Atlantic")

And the function can be applied to individual predators as well:

plotPreytrend(dat=preddiet4prey%>%filter(comname=="SUMMER FLOUNDER",
                                         epu=="MAB"), 
                metric="relmsw", 
                title="Summer Flounder in the Mid-Atlantic")

Combining plots

Perhaps we can put the composition plots next to the trends for each species to make it clear what we are seeing.

p1 <- plotDietCompBar(dat=aggdiet4prey%>%filter(season=="FALL", epu %in% c("MAB")), 
                metric="rave_amt", 
                title="Full diet composition")

p2 <- plotPreytrend(dat=aggdiet4prey%>%filter(season=="FALL", epu %in% c("MAB")), 
                metric="rave_amt", 
                title="Trends in components")

p1 + p2 + plot_layout(widths = c(4, 3), guides = 'collect') + plot_annotation(
  title = 'All predators combined, Mid-Atlantic in Fall')

We may still want to adjust the colors; the purple for PELINV is too similar to the purple line inidicating a significant downward trend.

All predators combined

Side-by-side plots for each region are in the tabs below (under construction…).

areas <- unique(aggdiet4prey$epu)
times <- unique(aggdiet4prey$season)

for(a in areas){
  for(t in times){
    p1 <- plotDietCompBar(dat=aggdiet4prey%>%filter(season==t, epu==a), 
                          metric="rave_amt", 
                          title="Full diet composition")
    
    p2 <- plotPreytrend(dat=aggdiet4prey%>%filter(season==t, epu==a), 
                        metric="rave_amt", 
                        title="Trends in components")
    cat("  \n###",  as.character(a),", ",  as.character(t),"  \n")
    print(p1 + p2 + plot_layout(widths = c(4, 3), guides = 'collect') + plot_annotation(
      title = paste0('All predators combined, ',a, ", ",t)))
    cat("  \n")   
  }
}

GB , FALL

GB , SPRING

GOM , FALL

GOM , SPRING

MAB , FALL

MAB , SPRING

SS , FALL

SS , SPRING

Selected individual predators

Side-by-side plots for a few predators and regions are in the tabs below (under construction…)